Making use of prognostics health management information for aerospace spare components logistics network optimisation

A cognitive system for fault prognosis in power transformers

“…In [47], the authors propose an ensemble of data-driven prognostic algorithms for robust prediction of RUL by the weighted sum of the outputs of five different methods: a Similarity-Based Interpolation (SBI) approach with Relevance Vector Machine (RVM) as the regression technique, SBI with Support Vector Machine (SVM), SBI with the least-square exponential fitting, a Bayesian linear regression with the least-square quadratic fitting, and a Recurrent Neural Network (RNN) approach. Specifically on fault prognosis in power transformers, it is possible to find some few recent studies in the literature, as the ones described in [35,36].…”

“…Another reason for the clustering of information is to allow the separation of agents in terms of functional classes, considering the kind of knowledge to be represented and the type of actuation required. In this way, higher system granularity can be achieved for the implementation of more complex systems applications, such as for instance health monitoring systems as described in [47]. The proposed architecture is a multiagent system that allows the use of different techniques together in an ensemble approach, for example, employing Bayesian networks, fuzzy models and statistical models.…”

Discrete Event Logistics Systems (DELS) simulation modeling incorporating two-step Remaining Useful Life (RUL) estimation

“…Fig. 1 depicts the basic profile in one-step RUL estimation [31]. The major parameters defining the one-step RUL estimation profile are listed below.…”

“…Zhiyu et al [30] makes the availability analysis by modeling the material flows and illustrating the maintenance decision-making under the CBM/PHM policy in a discrete event logistics system. Julka et al [31] present a comprehensive roadmap to investigate the optimal performance of the consolidated operational PHM information supported aerospace spare components logistics. The all-round aspects such as spare component delivery time, pool stock distributions are carefully optimized in comparisons to afford the maximum utility of the material supply chain.…”

Towards a generic prognostic function of technical multi-component systems taking into account the uncertainties of the predictions of their components

“…Therefore, one of the main challenges for companies is to keep these systems working with the highest level of dependability at the lowest cost. The implementations of Condition-Based Maintenance (CBM) and Prognostic and Health Management (PHM) concepts generally leads to improve equipment availability and to reduce maintenance costs [1,2,3].…”